Application of neural networks techniques to military pharmaceutical ordering problems
Name
44021679-MIT.pdf
Description
Full printable version
Size
4.43 MB
Format
Adobe PDF
Checksum (MD5)
ca3f1e7f90e77b7323eabf80e6b83327
Author(s)
Novak, John J. (John Joseph), 1971-
Advisor(s)
Amar Gupta.
Date Issued
1999
Publisher
Massachusetts Institute of Technology
Abstract
The military is one of the largest pharmaceutical distributors in the country. In order to minimize the amount of inventory held, and hence warehousing and expired drug costs, data mining techniques can be applied to old transaction records to predict future needs. One powerful method of data mining is the use of neural networks. Neural networks have the ability to learn inventory needs based on past situations which are expected to occur again. Using neural networks to data mine government pharmaceutical supply necessities will enable the reduction of inventory levels as well as improve customer satisfaction by increasing the chance the needed prescriptions will be in stock. This thesis introduces inventory methods, data mining methods, and explores the application of data mining and neural network methods to actual inventory optimization problems. Limits and future direction suggestions are included at the end of the document.
Description
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 68-69).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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